Book Image

Natural Language Processing with Java Cookbook

By : Richard M. Reese
Book Image

Natural Language Processing with Java Cookbook

By: Richard M. Reese

Overview of this book

Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon Web Services (AWS). You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentence, or semantic word.
Table of Contents (14 chapters)

Using regular expressions to find entities

Entities can be extracted using regular expressions. In this recipe, we will illustrate this process for email addresses. The code can be easily modified to address other entity types.

Regular expressions are sequences of special characters that describe a particular type of text. There will often be specialized units of text, such as email addresses or phone numbers, that possess a unique pattern. Regular expressions are used to describe these patterns and are used to find the elements in text.

Regular expressions can be difficult to read and understand. This can make the code more difficult to maintain. However, they are not as computationally intensive as neural networks can be. In addition, for many entities there are multiple, readily available regular expression variations easily found on the internet (https://www.vogella.com/tutorials...